{
  "abstract": "Transfer learning improves target-task performance by leveraging related source data. Most methods assume shared feature spaces, yet in many applications, each source observes only a subset of target covariates. Classical imputation fails here due to block missingness, and standard imputation matrices are not optimized for target parameter estimation. We study low- and high-dimensional linear regression and propose Heterogeneous Importance Weighting (HIW). Our method aligns feature spaces via projection-based imputation and transfers information through sample-selected importance weighting. This framework accommodates diverse projection matrices to construct target-oriented imputation. We develop a classification-based procedure with pseudo-responses to estimate conditional error densities for the weights. We establish entry-wise and global convergence rates for the estimator, with numerical and real-data studies demonstrating its effectiveness.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.08526",
  "author_total": 3,
  "authors": [
    {
      "name": "Zejing Zheng"
    },
    {
      "name": "Rui Huang"
    },
    {
      "name": "Junlong Zhao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/arws4s801h1d1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.08526",
  "fulltext_url": null,
  "id": "arws4s801h1d1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.08526",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1702|Domain Adaptation and Few-Shot Learning"
    ]
  ],
  "openalex": "W7212053781",
  "pdf_url": "https://arxiv.org/pdf/2609.08526",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/arws4s801h1d1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:07Z",
      "native_id": "2609.08526",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.08526"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Transfer Learning with Heterogeneous Feature Spaces in Linear Regression",
  "title_text": "Transfer Learning with Heterogeneous Feature Spaces in Linear Regression",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:07Z",
  "url": "https://research.draconitic.com/paper/arws4s801h1d1",
  "venue": "arXiv",
  "versions": []
}